What is the population now? 7.7 billion! This is not a hard question at all. Google it, and you will get the real-time number. Look, we are surrounded by data or big data.

What is big data?

As the conception is saying, big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.

In a word, big data is a large amount of number that can be analyzed to make business decisions.

What does big data mean for marketing?

Two weeks before, I discussed the relationship between big data and privacy. But as a future marketer, I care more about what can big data do for marketing.

Big data is providing insights into which content is the most effective at each stage of a sales cycle, how Investments in Customer Relationship Management (CRM) systems can be improved, in addition to strategies for increasing conversion rates, prospect engagement, conversion rates, revenue and customer lifetime value. For cloud-based enterprise software companies, big data provides insights into how to lower the Customer Acquisition Cost (CAC), Customer Lifetime Value (CLTV), and manage many other customer-driven metrics essential to running a cloud-based business. Big data triggers marketing decisions telltale signs.

However, big data is not the almighty God!

Although the big data brings great convenience to marketers, it has several limitations as follows:

1. User Data Is Fundamentally Biased. Does every data tell the truth? NO! Customers’ footprints are hard to track. Additionally, even some of the customers’ behaviors do not show their exact preference, then how can marketers analysis use information from biased data
2. User-Level Execution Only Exists In Select Channels. Not every channel could  apply user data. Take offline channels and premium display as examples, user-level data cannot be applied to execution at all.
3. User-Level Results Cannot Be Presented Directly. This limitation means that big data analysis is professional work, and this work tends to be incomprehensible to all but domain experts.
4. User-Level Algorithms Have Difficulty Answering “Why.”Big data anlysis and and logical analysis are different from each other. Sometimes, average marketers tend to have difficulty answering “why” questions.
5. User Data Is Not Suited For Producing Learnings.Not everything that big data reveals will work out in marketing area.
6. User-Level Data Is Subject To More Noise. Sometimes, the data makes marketers confused. How to deal with the data is a difficulty needed to be solved. For example, a particular cookie received – for whatever reason – a hundred display impressions in a row from the same website within an hour (happens much more often than you might think). Should this be treated as a hundred impressions or just one, and how will it affect your analysis results?
7. User Data Is Not Easily Accessible Or Transferable.Because of security consciousness, it is impossible that people could visit the database that they want to access, which makes it difficult for team members to conduct follow-up analyses and validation.

Overall, as a marketer, using big data analysis as a tool for decisions making, also putting yourself in the market environment are both critical. Hope big data make you a good marketer!